Monthly Inflow Forecasting of Soyang River Dam Using VARMA and Machine Learning Models

Bae, Younghye; 토목공학과, 인하대학교; Kim, Jongsung; Wang, Wonjoon; Yoo, Younghoon; Jung, Jaewon; Kim, Hung Soo

doi:10.14383/cri.2019.14.3.183

Cited by 4 publications

(2 citation statements)

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“…There are many research papers about dam inflow prediction using diverse statistical methods (Kim et al 2017;Bae et al 2019;Hong et al 2020;Han et al 2021). For example, Awan & Bae (2014) developed an Adaptive Neuro-Fuzzy Inference System (ANFIS)-based model for dam inflow prediction for six major dams, including the Andong dam and Chungju dam in South Korea, using monthly precipitation, relative humidity, and air temperature.…”

Section: Introductionmentioning

confidence: 99%

Dam inflow prediction using large-scale climate variability and deep learning approach: a case study in South Korea

et al. 2023

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Accurate prediction of dam inflows is essential for effective water resources management in terms of both water quantity and quality. This study aims to develop a Long Short-Term Memory (LSTM) deep learning-based monthly dam inflow prediction model using large-scale climate indices. Six climate indices, Atlantic multidecadal oscillation (AMO), El Niño–southern oscillations (ENSO), North Atlantic oscillation (NAO), Pacific decadal oscillation (PDO), Niño 3.4, and Southern Oscillation Index (SOI) for the period of 1981–2020, were used as input variables of the model. The proposed model was trained with 29 years of data (1981–2009) and tested with 12 years of data (2009–2020). We investigated 29 input data combinations to evaluate the predictive performance according to different input datasets. The model showed the average values of metrics ranged from 0.5 to 0.6 for CC and from 40 to 80 cm for root mean square error (RMSE) at three dams. The prediction results from the model showed lower performance as the lead time increased. Also, each dam showed different prediction results for different seasons. For example, Soyangriver/Daecheong dams have better accuracy in prediction for the wet season than the dry season, whereas the Andong dam has a high prediction ability during the dry season. These investigations can be used for better efficient dam management using a data-driven approach.

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Section: Introductionmentioning

confidence: 99%

Dam inflow prediction using large-scale climate variability and deep learning approach: a case study in South Korea

et al. 2023

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“…과거의 경우 수위 변화에 영향을 미치는 인자들을 모두 고려한 후 물리적 모형을 구축해 모든 영향인자들과 수위 사이의 상관관계 및 인과관계를 분석하는 것은 불가능하였 다. 그러나 많은 양의 정보 수집의 가능함과 함께 빅데이터의 처리가 가능한 머신러닝(Machine Learning)의 발전으로 인 해 입력 및 출력자료 기반 학습 및 예측에 대한 연구가 진행되었다 (Riad et al, 2004;Abrahart et al, 2004;Ghumman et al, 2011;Granata et al, 2016;Mosavi et al, 2018;Yan et al, 2018;Bae et al, 2019). 머신러닝을 통한 수위예측의 연구가 진행됨에 따라 머신러닝 기법 중 인간의 두뇌 신경망 구조를 모방해 개발된 딥러닝(Deep Learning)을 통한 수위예 측의 연구가 진행되었다 (Chen et al, 2013;Shoaib et al, 2016;Assem et al, 2017;Park et al, 2018;Choi et al, 2020;Kim et al, 2020;Lee et al, 2020) (1) 및 Eq.…”

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Application of Neural Networks to Predict Daecheong Dam Water Levels

Ryu¹,

Lee²

2022

J. Korean Soc. Hazard Mitig

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The accurate prediction of water levels is essential for the efficient operation of hydraulic structures such as dams. However, such predictions are impossible when considering all factors affecting water level changes. Recently, with the development of artificial neural networks, there has been research on water level prediction using deep learning models. In this study, the deep learning technique of Multi-Layer Perceptron (MLP) was combined with Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) techniques, which afford advantages in time series data processing to predict the water level of Daecheong Dam. Three data preprocessing processes were applied to improve accuracy in water level predictions by taking travel time into account, adding the outflow data of the dam, and normalizing the data. Eight cases were created with the three data preprocessing processes. When normalization was performed for the data that took travel time into account and the data were then used as input data for LSTM, the best prediction result was found for the peak water level of the dam. If various other data preprocessing techniques are applied in addition to those applied in this study, RNN and LSTM will show improved prediction results.

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Comparative analysis of long short-term memory and storage function model for flood water level forecasting of Bokha stream in NamHan River, Korea

Kim

Lee²,

Kim

et al. 2022

Journal of Hydrology

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Monthly Inflow Forecasting of Soyang River Dam Using VARMA and Machine Learning Models

Cited by 4 publications

References 0 publications

Dam inflow prediction using large-scale climate variability and deep learning approach: a case study in South Korea

Dam inflow prediction using large-scale climate variability and deep learning approach: a case study in South Korea

Application of Neural Networks to Predict Daecheong Dam Water Levels

Comparative analysis of long short-term memory and storage function model for flood water level forecasting of Bokha stream in NamHan River, Korea

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